Ad-hoc ride sharing application using continuous SPARQL queries

Mukherjee, Debnath; Banerjee, Snehasis; Misra, Prateep

doi:10.1145/2187980.2188136

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Domestic structures and international institutions have done some research about the carpooling service [3][4][5]. Many scholars begin to study mobile phone-based carpooling service software.…”

Section: Related Workmentioning

confidence: 99%

Social Group Architecture Based Distributed Ride-Sharing Service in VANET

Zhao

Qin

Yang

et al. 2014

International Journal of Distributed Sensor Networks

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Lots of traditional distributed ride-sharing services match vehicles with drivers and passengers geographic information only. When urban road congestion situation is particularly serious, such as Beijing's rush hour, the waiting time for the passengers is too long which affects the quality of the service. So we propose a distributed ride-sharing service based on dual Social Group Architecture (SGA). We divide the service into Drivers Social Group Architecture (DSGA) message and Vehicles Social Group Architecture (VSGA) message. Vehicles generate dual SGA messages to complete ride-sharing service. DSGA messages focus on the relation between drivers and passengers. We make a basic geometry matching by generating the DSGA messages. VSGA messages figure out the traffic conditions through a multilevel detection. After generating VSGA messages, the final matching strategy is processed. The low level detection is finished by the limited neighbors to decrease the network consume. Dual SGA messages shorten the waiting time for the passengers and avoid traffic jams. Analysis shows that our scheme enhances the ride-sharing service quality and robust.

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Section: Related Workmentioning

confidence: 99%

Social Group Architecture Based Distributed Ride-Sharing Service in VANET

Zhao

Qin

Yang

et al. 2014

International Journal of Distributed Sensor Networks

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“…Applications like road condition monitoring and alert [20], ad-hoc ride sharing [21],energy information gateway for home [22] have already been integrated within this platform.…”

Section: Workload Types Of Iot Applicationsmentioning

confidence: 99%

Challenges of Using Edge Devices in IoT Computation Grids

Dey¹,

Mukherjee²,

Paul³

et al. 2013

2013 International Conference on Parallel and Distributed Systems

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Internet of Things (IoT) has the potential to become a technology revolution with a vision of creating very large scale network, comprising of unprecedented number of connected devices. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, embedded computers. There has been sustained research work and standardization effort from different IoT perspectives like integration of sensor and RFID devices to the Internet. With the increasing trend of gathering business insights from unstructured data, the high volume of data generated by such devices is also of interest. Cloud based data mining platforms are suitable for analyses of such data and researchers have proposed architectures where personal mobile phones can act as Edge Gateway between the sensor network and cloud analytics platform. It seems that the surge in the volume of data generated by huge number of Smart Items can only be matched if a large percentage of mobile users start sharing the computation capability of their personal devices and work together towards true Participatory Computing in the IoT systems. In this work we try to understand the challenges associated with running computation jobs on the mobile devices using different types of workload often observed in IoT applications. Based on the insights gained from experiments performed by us, we propose a scheme where mobile phones, residential gateways and other edge devices offer free slots to servers in a cloud based data analytics system. Based on the free time slots offered by the mobile phones, if commensurately sized computational jobs can be scheduled, the unpredictability associated with using mobile phones as grid resources can be solved.

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“…4) Application managed window: In stream reasoning scenario, sometimes the aforementioned windowing mechanisms fail. The concept of applications controlling the window tuples in a custom fashion is described in [3] and [4]. As an example, a critical event E like 'Fire at a building' may last for 4 hours, however if a time window of 3 hours is used, E will get deleted.…”

Section: Types Of Window Mechanismsmentioning

confidence: 99%

Windowing mechanisms for web scale stream reasoning

Banerjee

Mukherjee

2013

Proceedings of the 4th International Workshop on Web-Scale Knowledge Representation Retrieval and Reasoning

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Web-scale stream reasoning is based on continuous queries and reasoning on a snapshot of the dynamic knowledge combined with background knowledge. The existing stream reasoners usually use either time-based or count-based window techniques following the data stream principles, however they do not fit all scenarios in the stream reasoning area. In this paper, different types of windowing mechanisms are described with exemplary scenarios in which they are most suitable for reasoning on stream of facts. A new windowing technique namely Adaptive Window is also proposed. Lastly, some important questions related to windowing techniques for web-scale stream reasoning are positioned.

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Ad-hoc ride sharing application using continuous SPARQL queries

Cited by 9 publications

References 4 publications

Social Group Architecture Based Distributed Ride-Sharing Service in VANET

Social Group Architecture Based Distributed Ride-Sharing Service in VANET

Challenges of Using Edge Devices in IoT Computation Grids

Windowing mechanisms for web scale stream reasoning

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